We train all models on images of \(256 \times 256\)  resolution, padded as necessary. See dataset statistics in table REF . The images in the Cityscapes and FFHQ datasets are mirror-augmented, while the images in the COCO dataset are both mirror-augmented and also randomly cropped, to increase the effective training set size. We assume access to a training data of image and panoptic segmentations [1]}, indicating the segment unique identity and its semantic class. Contrary to prior conditional works which rely on costly hand-annotated ground-truth segmentations, and to demonstrate the model robustness, we instead intentionally explore training on auto-predicted segmentations (either produced in an unsupervised manner [2]} for CLEVR [3]} or by pre-trained segmentor [4]} otherwise).
